Models love a founder's story and can't tell a real market from a rounded-up one: a TAM built by multiplying optimism, traction metrics that don't cohort, a moat that's really just a feature.
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Models reach for an actuarial answer and fumble the long-tail math: a reserve that ignores development, mortality assumptions applied to the wrong cohort, discounting that quietly breaks the present value.
Ask a model to assess patentability and it reasons smoothly, then treats an abstract idea as eligible subject matter, waves past the closest prior art, and construes a claim in a way no examiner would accept.
Models write an equity note that sounds like the sell side and falls apart under a covering analyst's read: an estimate that ignores guidance, a target price with no bridge from the model, a thesis that forgets the last three quarters actually happened.
Hand a model a revenue arrangement and it writes a confident memo, then misapplies the ASC 606 steps, parks a lease in the wrong place under ASC 842, drops a required disclosure, and presents a non-GAAP measure the SEC would flag.
Give a model a contract to redline and it produces clean-looking edits that quietly leave an obligation with no trigger, a defined term used two different ways, and a cross-reference pointing at the wrong section. The one-sided indemnity survives untouched.
Models give financial advice that sounds reassuring and quietly ignores the client: a tax-blind withdrawal plan, an allocation that forgets the time horizon, an estate suggestion that would fall apart in probate.
Models produce documentation that reads well and fails on integrity: a note that does not support the code billed, a missed query opportunity, a present-on-admission indicator that shifts an MS-DRG, a casual handling of protected health information.
Models approve a borrower that any underwriter would flag: cash-flow coverage computed off the wrong line, covenants that don't bite, a rating that ignores where we are in the cycle. Someone has to find the default risk hiding in a clean-looking spread, and that someone is you.
Models explain managed care in tidy theory and miss the mechanics that decide coverage: a benefit rule that overrides the general one, a prior-auth requirement waived in the contract, a utilization review that should have flagged the case.
Ask a model to build a forecast and it returns a clean-looking model whose drivers do not connect, whose assumptions defy the business, and whose variance bridge does not tie to actuals. The formatting is immaculate; the logic leaks.
Models recite cardiology confidently and still miss what matters: an outdated guideline threshold, a misread rhythm strip, an anticoagulation choice that ignores a clear contraindication.
Ask a model about a regulatory obligation and it answers with confidence, then cites a rule that was superseded two years ago, blurs the line between binding regulation and informal guidance, and applies a European regime to a US filing. Reporting thresholds come out wrong.
Models build an LBO that returns a tidy IRR and skip the parts that make or break a deal: a debt schedule that ignores the covenants, an entry multiple with no exit story, diligence that takes management's projections at face value.
A model will design an audit approach that reads well and would never survive review: a sample that tests the wrong assertion, a control it calls effective without evidence, a materiality call set where it is convenient.
Models sound reassuring and still get bedside care wrong: a dosing detail that no nurse would administer, a triage order that buries the sickest patient, discharge teaching that misses the one thing that keeps someone safe at home.
Hand a model a merger agreement and it summarizes it fluently, then misses that the indemnification cap swallows the escrow, that a closing condition has no matching covenant, and that the chosen deal structure triggers a tax result no client would accept.
Models write a pitch deck's worth of confident deal analysis and miss what a deal team lives on: a comps set stuffed with the wrong peers, an accretion/dilution model that ignores the financing, a synergy number pulled from thin air.
Models describe the revenue cycle in clean theory and miss the mechanics that decide whether a claim pays: the real reason behind a CARC or RARC denial code, a payer rule that overrides the general one, an eligibility check that should have happened before the visit.
Ask a model to plan an audit and it produces a tidy program, then sets materiality by feel, accepts a management assertion as if it were evidence, and designs a sample that would never support the conclusion it draws. Professional skepticism is exactly what it lacks.
Ask a model to brief a motion and it hands back something that reads like a real filing, then cites three cases that do not exist and misstates the standard of review on a fourth. The procedural posture drifts, the deadline math is off, and the local rules are ignored.
Models assign codes that look right and bill wrong: an ICD-10-CM code one notch short on specificity, a CPT modifier that changes the whole claim, an NCCI edit ignored, an E&M level the note never supported.
Models can scaffold a whole app in one shot: front end, API, and database, all of it looking right. Then the state goes stale, the endpoint leaks data, and the migration bricks production.
Models produce confident accounting answers that fail on the details: misapplied standards, broken reconciliations, and journal entries that don't balance.